Adaptive Algorithms for Model Order Selection problems

Model order selection problems are important for signal processing and its various applications in wireless communications, radar theory, navigation, control theory, and others. We described three model order selection algorithms whose penalty terms depend on the observed data and apply these algorithms to the problem of estimating the number of signals with unknown amplitudes. Performance analysis for these algorithms is carried out using the error probability as the performance measure. Based on the results of the performance analysis we find optimal values of tuning parameters of the algorithms where needed and compare all algorithms with each other.